Multiple-source adaptation theory and algorithms

We present a general theoretical and algorithmic analysis of the problem of multiple-source adaptation, a key learning problem in applications. We derive new normalized solutions with strong theoretical guarantees for the cross-entropy loss and other similar losses. We also provide new guarantees th...

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Veröffentlicht in:	Annals of mathematics and artificial intelligence Jg. 89; H. 3-4; S. 237 - 270
Hauptverfasser:	Zhang, Ningshan, Mohri, Mehryar, Hoffman, Judy
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.03.2021 Springer Springer Nature B.V
Schlagworte:	Adaptation Algorithms Analysis Artificial Intelligence Complex Systems Computational linguistics Computer Science Empirical analysis Entropy (Information theory) Language processing Mathematics Natural language interfaces Domain adaptation DC programming Rényi divergence 68T05 Multiple-source adaptation Transfer learning
ISSN:	1012-2443, 1573-7470
Online-Zugang:	Volltext
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